SoroushVahidi/scidocs huggingface dataset:
A Secure Low-Cost Edge Device Authentication Scheme for the Internet of Things
(2018)

Because of the enhanced capability of adversaries, edge devices of Internet of Things (IoT) infrastructure are now increasingly vulnerable to counterfeiting and piracy. Ensuring the authenticity of such devices is of great concern since an adversary can create a backdoor either to bypass the security, and/or to leak secret information over an unsecured communication channel. The reliability of such devices could also be called into question because they might be counterfeit, defective and/or of inferior quality. It is of prime importance to design and develop solutions for authenticating such edge devices. In this paper, we present a novel low-cost solution for authenticating edge devices. We use SRAM based PUF to generate unique "digital fingerprints" for every device, which can be used to generate a unique device ID. We propose a novel ID matching scheme to verify the identity of an edge device even though the PUF is extremely unreliable. We show that the probability of impersonating an ID by an adversary is extremely low. In addition, our proposed solution is resistant to various known attacks.
A Survey of Secure Routing Protocols in Multi-Hop Cellular Networks
(2018)

Multi-hop networks are expected to be an important part of 5G mobile systems. In a multi-hop cellular network (MCN), the nodes assist each other in relaying packets towards their destinations. The structures and operations of multi-hop networks make them particularly vulnerable to certain types of attack. Hence, security measures to counter these attacks are necessary. In this paper, we provide an overview of the secure routing protocols for multi-hop networks and classify them into MCN Type-1 and Type-0 categories for device-to-device communications, and the Internet-of-Things category for machine-to-machine communications. Our focus is on the applied cryptographic techniques and the security mechanisms in secure routing protocols. We propose an evaluation framework incorporating different security aspects, vulnerabilities, and levels of deployability to compare a number of secure routing protocols. Moreover, we review the secure routing paths in software-defined networking as a solution for existing challenges in multi-hop networks. Some open research problems are highlighted as possible directions for future studies.
A 2d + 3d rich data approach to scene understanding
(2013)

On your one-minute walk from the coffee machine to your desk each morning, you pass by dozens of scenes – a kitchen, an elevator, your office – and you effortlessly recognize them and perceive their 3D structure. But this one-minute scene-understanding problem has been an open challenge in computer vision since the field was first established 50 years ago. In this dissertation, we aim to rethink the path researchers took over these years, challenge the standard practices and implicit assumptions in the current research, and redefine several basic principles in computational scene understanding. The key idea of this dissertation is that learning from rich data under natural setting is crucial for finding the right representation for scene understanding. First of all, to overcome the limitations of object-centric datasets, we built the Scene Understanding (SUN) Database, a large collection of real-world images that exhaustively spans all scene categories. This scene-centric dataset provides a more natural sample of human visual world, and establishes a realistic benchmark for standard 2D recognition tasks. However, while an image is a 2D array, the world is 3D and our eyes see it from a viewpoint, but this is not traditionally modeled. To obtain a 3D understanding at high-level, we reintroduce geometric figures using modern machinery. To model scene viewpoint, we propose a panoramic place representation to go beyond aperture computer vision and use data that is close to natural input for human visual system. This paradigm shift toward rich representation also opens up new challenges that require a new kind of big data – data with extra descriptions, namely rich data. Specifically, we focus on a highly valuable kind of rich data – multiple viewpoints in 3D – and we build the SUN3D database to obtain an integrated place-centric representation of scenes. We argue for the great importance of modeling the computer’s role as an agent in a 3D scene, and demonstrate the power of place-centric scene representation. Thesis Supervisor: Antonio Torralba Title: Associate Professor
A Deep Reinforcement Learning Algorithm with Expert Demonstrations and Supervised Loss and its application in Autonomous Driving
(2018)

In this paper, we propose a deep reinforcement learning(DRL) algorithm which combines Deep Deterministic Policy Gradient (DDPG) with expert demonstrations and supervised loss for decision making for autonomous driving. Training DRL agent with supervised learning is adopted to accelerate the exploration process and increase the stability. A supervised loss function is introduced in the algorithm to update the actor networks. In addition, reward construction is combined to make the training process more stable and efficient. The proposed algorithm is applied to a popular autonomous driving simulator called TORCS. The experimental results show that the training efficiency and stability are improved by utilizing our algorithm in autonomous driving.
Residual Useful Life Prediction for Rolling Element Bearings Based on Multi-feature Fusion Regression
(2017)

Residual useful life (RUL) prediction is critical in efficient implementation of condition-based maintenances for rolling element bearings (REBs). A multi-feature fusion regression method is reported for predicting the RUL of REBs in this work. In the proposed approach, locally linear embedding technique is employed to fuse original features for erecting a condition indicator of the REBs. The fused condition indicator is then modeled by an adaptive network-based fuzzy inference system for predicting the RUL. The present approach is applied to experimental data collected from an REB. Experimental results show that the proposed approach exhibits better performance than peer approaches. It is capable of accurately predicting the RUL of the REB.
Deep residual coalesced convolutional network for efficient semantic road segmentation
(2017)

This paper proposes a deep learning-based efficient and compact solution for road scene segmentation problem, named deep residual coalesced convolutional network (RCC-Net). Initially, the RCC-Net performs dimensionality reduction to compress and extract relevant features, from which it is subsequently delivered to the encoder. The encoder adopts the residual network style for efficient model size. In the core of each residual network, three different convolutional layers are simultaneously coalesced for obtaining broader information. The decoder is then altered to upsample the encoder for pixel-wise mapping from the input images to the segmented output. Experimental results reveal the efficacy of the proposed network over the state-of-the-art methods and its capability to be deployed in an average system.
Text-Independent Writer Identification via CNN Features and Joint Bayesian
(2016)

This paper proposes a novel method for offline text-independent writer identification by using convolutional neural network (CNN) and joint Bayesian, which consists of two stages, i.e. feature extraction and writer identification. In the stage of feature extraction, since a large number of data is essential to train an effective CNN model with high generalizability and the amount of handwriting is limited in writer identification, a data augmentation technique is first developed to generate thousands of handwriting images for each writer. Then a deep CNN network is designed to extract discriminative features to represent the properties of different writing styles, which is trained by using the generated handwriting images. In the stage of writer identification, the training dataset is used to train the CNN model for feature extraction and the joint Bayesian technique is employed to accomplish the task of writer identification based on the extracted CNN features. The proposed method is tested on two standard benchmark datasets, i.e. ICDAR2013 and CVL dataset. Experimental results demonstrate that the proposed method gets the best performance compared to the state-of-the-art approaches.
Mental health of hospital consultants: the effects of stress and satisfaction at work
(1996)

BACKGROUND
Burnout and psychiatric morbidity among gastroenterologists, surgeons, radiologists, and oncologists in the UK have been estimated by means of a questionnaire-based survey. The relationship between consultants' mental health and their job stress and satisfaction, as well as their job and demographic characteristics, were also examined.


METHODS
Psychiatric morbidity was estimated using the 12-item General Health Questionnaire. The three components of burnout-emotional exhaustion, depersonalization, and low personal accomplishment-were assessed using the Maslach Burnout Inventory. Job stress and satisfaction were measured using study-specific questions.


FINDINGS
Of 1133 consultants, 882 (78%) returned questionnaires. The estimated prevalence of psychiatric morbidity was 27%, with no significant differences between the four specialist groups. Radiologists reported the highest level of burnout in terms of low personal accomplishment. Job satisfaction significantly protected consultants' mental health against job stress. Three sources of stress were associated with both burnout and psychiatric morbidity; feeling overloaded, and its effect on home life; feeling poorly managed and resourced; and dealing with patients' suffering. Burnout was also associated with low satisfaction in three domains: relationships with patients, relatives and staff; professional status/esteem; intellectual stimulation. In addition, being aged 55 years or less and being single were independent risk factors for burnout. Burnout was also more prevalent among consultants who felt insufficiently trained in communication and management skills.


INTERPRETATION
Consultants' mental health is likely to be protected against the high demands of medical practice by maintaining or enhancing job satisfaction, and by providing training in communication and management skills.
Progressive skyline computation in database systems
(2005)

The skyline of a d-dimensional dataset contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in the database community, especially for progressive methods that can quickly return the initial results without reading the entire database. All the existing algorithms, however, have some serious shortcomings which limit their applicability in practice. In this article we develop branch-and-bound skyline (BBS), an algorithm based on nearest-neighbor search, which is I/O optimal, that is, it performs a single access only to those nodes that may contain skyline points. BBS is simple to implement and supports all types of progressive processing (e.g., user preferences, arbitrary dimensionality, etc). Furthermore, we propose several interesting variations of skyline computation, and show how BBS can be applied for their efficient processing.
The Skyline Operator
(2001)

We propose to extend database systems by a Skyline operation. This operation $filters out a set of interesting points from a potentially large set of data points. A point is interesting if it is not dominated by any other point. For example, a hotel might be interesting for somebody traveling to Nassau if no other hotel is both cheaper and closer to the beach. We show how SQL can be extended to pose Skyline queries, present and evaluate alternative algorithms to implement the Skyline operation, and show how this operation can be combined with other database operations, e.g., join.
PEDOT:PSS "Wires" Printed on Textile for Wearable Electronics.
(2016)

Herein, the fabrication of all-organic conductive wires is demonstrated by utilizing patterning techniques such as inkjet printing and sponge stencil to apply poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS) onto nonwoven polyethylene terephthalate (PET) fabric. The coating of the conducting polymer is only present on the surface of the substrate (penetration depth ∼ 200 μm) to retain the functionality and wearability of the textile. The wires fabricated by different patterning techniques provide a wide range of resistance, i.e., tens of kΩ/□ to less than 2 Ω/□ that allows the resistance to be tailored to a specific application. The sheet resistance is measured to be as low as 1.6 Ω/□, and the breakdown current is as high as 0.37 A for a 1 mm wide line. The specific breakdown current exceeds the previously reported values of macroscopic carbon nanotube based materials. Simple circuits composed of the printed wires are demonstrated, and resistance of the circuit from the measurement agrees with the calculated value based on Kirchhoff's rules. Additionally, the printed PEDOT:PSS wires show less than 6.2% change in sheet resistance after three washing and drying cycles using detergent.
A neurocognitive approach to understanding the neurobiology of addiction
(2013)

Recent concepts of addiction to drugs (e.g. cocaine) and non-drugs (e.g. gambling) have proposed that these behaviors are the product of an imbalance between three separate, but interacting, neural systems: an impulsive, largely amygdala-striatum dependent, neural system that promotes automatic, habitual and salient behaviors; a reflective, mainly prefrontal cortex dependent, neural system for decision-making, forecasting the future consequences of a behavior, and inhibitory control; and the insula that integrates interoception states into conscious feelings and into decision-making processes that are involved in uncertain risk and reward. These systems account for poor decision-making (i.e. prioritizing short-term consequences of a decisional option) leading to more elevated addiction risk and relapse. This article provides neural evidence for this three-systems neural model of addiction.
Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction
(2015)

This study investigates the use of unsupervised features derived from word embedding approaches and novel sequence representation approaches for improving clinical information extraction systems. Our results corroborate previous findings that indicate that the use of word embeddings significantly improve the effectiveness of concept extraction models; however, we further determine the influence that the corpora used to generate such features have. We also demonstrate the promise of sequence-based unsupervised features for further improving concept extraction.
Fog computing: Security issues, solutions and robust practices
(2017)

Fog Computing (FC) has extended the services of cloud computing to the edge of the network. It inherits some of the characteristics from cloud computing but FC also have some distinguished features such as geo-distribution, location awareness and low latency. Along with the inherited characteristics, it also inherits the issues and problems of cloud computing like energy efficiency, resource management and security issues. This paper presents the critical analysis of the fog architecture with respect to security. The state of the art work done since 2012 is critical analyzed on the bases of security techniques and security threats. We grouped the existing security techniques on the bases of security goals achieved by each. It will provide a clear and comprehensive distinction between the security areas explored and those, which still need researchers' attention.
Specification Inference from Demonstrations
(2017)

Learning from expert demonstrations has received a lot of attention in artificial intelligence and machine learning. The goal is to infer the underlying reward function that an agent is optimizing given a set of observations of the agent’s behavior over time in a variety of circumstances, the system state trajectories, and a plant model specifying the evolution of the system state for different agent’s actions. The system is often modeled as a Markov decision process (Puterman 2014), that is, the next state depends only on the current state and agent’s action, and the the agent’s choice of action depends only on the current state. While the former is a Markovian assumption on the evolution of system state, the later assumes that the target reward function is itself Markovian. In this work, we explore learning a class of non-Markovian reward functions, known in the formal methods literature as specifications. These specifications offer better composition, transferability, and interpretability. We then show that inferring the specification can be done efficiently without unrolling the transition system. We demonstrate on a 2-d grid world example.
Comparative study of trust and reputation systems for wireless sensor networks
(2013)

Wireless sensor networks (WSNs) are emerging as useful technology for information extraction from the surrounding environment by using numerous small-sized sensor nodes that are mostly deployed in sensitive, unattended, and (sometimes) hostile territories. Traditional cryptographic approaches are widely used to provide security in WSN. However, because of unattended and insecure deployment, a sensor node may be physically captured by an adversary who may acquire the underlying secret keys, or a subset thereof, to access the critical data and/or other nodes present in the network. Moreover, a node may not properly operate because of insufficient resources or problems in the network link. In recent years, the basic ideas of trust and reputation have been applied to WSNs to monitor the changing behaviors of nodes in a network. Several trust and reputation monitoring (TRM) systems have been proposed, to integrate the concepts of trust in networks as an additional security measure, and various surveys are conducted on the aforementioned system. However, the existing surveys lack a comprehensive discussion on trust application specific to the WSNs. This survey attempts to provide a thorough understanding of trust and reputation as well as their applications in the context of WSNs. The survey discusses the components required to build a TRM and the trust computation phases explained with a study of various security attacks. The study investigates the recent advances in TRMs and includes a concise comparison of various TRMs. Finally, a discussion on open issues and challenges in the implementation of trust-based systems is also presented. Copyright © 2012 John Wiley & Sons, Ltd.
Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands.
(2012)

Cortical neural prosthetics extract command signals from the brain with the goal to restore function in paralyzed or amputated patients. Continuous control signals can be extracted from the motor cortical areas, whereas neural activity from posterior parietal cortex (PPC) can be used to decode cognitive variables related to the goals of movement. Because typical activities of daily living comprise both continuous control tasks such as reaching, and tasks benefiting from discrete control such as typing on a keyboard, availability of both signals simultaneously would promise significant increases in performance and versatility. Here, we show that PPC can provide 3D hand trajectory information under natural conditions that would be encountered for prosthetic applications, thus allowing simultaneous extraction of continuous and discrete signals without requiring multisite surgical implants. We found that limb movements can be decoded robustly and with high accuracy from a small population of neural units under free gaze in a complex 3D point-to-point reaching task. Both animals' brain-control performance improved rapidly with practice, resulting in faster target acquisition and increasing accuracy. These findings disprove the notion that the motor cortical areas are the only candidate areas for continuous prosthetic command signals and, rather, suggests that PPC can provide equally useful trajectory signals in addition to discrete, cognitive variables. Hybrid use of continuous and discrete signals from PPC may enable a new generation of neural prostheses providing superior performance and additional flexibility in addressing individual patient needs.
InverseRenderNet: Learning single image inverse rendering
(2018)

We show how to train a fully convolutional neural network to perform inverse rendering from a single, uncontrolled image. The network takes an RGB image as input, regresses albedo and normal maps from which we compute lighting coefficients. Our network is trained using large uncontrolled image collections without ground truth. By incorporating a differentiable renderer, our network can learn from self-supervision. Since the problem is ill-posed we introduce additional supervision: 1. We learn a statistical natural illumination prior, 2. Our key insight is to perform offline multiview stereo (MVS) on images containing rich illumination variation. From the MVS pose and depth maps, we can cross project between overlapping views such that Siamese training can be used to ensure consistent estimation of photometric invariants. MVS depth also provides direct coarse supervision for normal map estimation. We believe this is the first attempt to use MVS supervision for learning inverse rendering.
Design of ultra high-speed CMOS CML buffers and latches
(2003)

A comprehensive study of ultra high-speed current-mode logic (CML) buffers and regenerative CML latches will be illustrated. A new design procedure to systematically design a chain of tapered CML buffers is proposed. Next, a new 20GHz regenerative latch circuit will be introduced. Experimental results show a higher performance for the new latch architecture compared to a conventional CML latch circuit at ultra high-frequencies. It is also shown, both through the experiments and by using efficient analytical models, why CML buffers are better than CMOS inverters in high-speed low-voltage applications.
Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images
(2011)

Extracting well-distributed, reliable, and precisely aligned point pairs for accurate image registration is a difficult task, particularly for multisource remote sensing images that have significant illumination, rotation, and scene differences. The scale-invariant feature transform (SIFT) approach, as a well-known feature-based image matching algorithm, has been successfully applied in a number of automatic registration of remote sensing images. Regardless of its distinctiveness and robustness, the SIFT algorithm suffers from some problems in the quality, quantity, and distribution of extracted features particularly in multisource remote sensing imageries. In this paper, an improved SIFT algorithm is introduced that is fully automated and applicable to various kinds of optical remote sensing images, even with those that are five times the difference in scale. The main key of the proposed approach is a selection strategy of SIFT features in the full distribution of location and scale where the feature qualities are quarantined based on the stability and distinctiveness constraints. Then, the extracted features are introduced to an initial cross-matching process followed by a consistency check in the projective transformation model. Comprehensive evaluation of efficiency, distribution quality, and positional accuracy of the extracted point pairs proves the capabilities of the proposed matching algorithm on a variety of optical remote sensing images.